ECG Diagnosis using Intel® oneAPI Libraries: Developer Spotlight

Electrocardiogram (ECG) is a diagnostic technique for checking various conditions of a human heart. It records electrical signals from the heart and tracks its rhythm through repeated cardiac cycles. Manual interpretation of ECG data is a time-consuming and error-prone process. An accurate interpretation of ECG data requires medical expertise which may not be adequately available in underserved communities, leading to serious health issues among heart patients.

Deepthi A J in her blog proposed an application for efficient auto-diagnosis of ECG that can serve as a life-saving tool among the underserved communities.

The blog talks about:

• Why is the ECG auto-diagnosis solution important?
• Steps involved in building and optimizing the solution.
• How were various Intel® oneAPI libraries used in the process?
• Data preprocessing techniques for preparing data for the auto-diagnosis application.
• How can Intel® DevCloud for oneAPI be used for leveraging Intel® oneAPI tools with scalability?

The proposed ECG auto-diagnosis application uses the following Intel® oneAPI libraries and modules:

1. Intel® oneAPI Deep Neural Networks (oneDNN) - for building a deep Convolutional Neural Network (CNN) to classify input ECG signals.
   
   
2. Intel® oneAPI Data Analytics Library (oneDAL) - It enables using TensorFlow* Optimizations from Intel for optimizing the neural network and Intel® Distribution for Python* for data preparation and analysis.
   
   
3. Intel® oneAPI Math Kernel Library (oneMKL) for complex mathematical computations within the network, such as Fast Fourier Transform (FFT) and random number generation.
   
   
4. Intel® oneAPI Threading Building Blocks (oneTBB) - for adding parallelism to the application so that it can leverage computational capabilities of multiple CPU cores.
   
   
5. Intel® oneAPI DPC++ Library (oneDPL) - for efficient cross-architecture implementation of the network across CPUs, GPUs and FPGAs.

Read the complete blog and know how Intel® oneAPI components helped in designing a highly accurate and performant ECG auto-diagnosis system.

What’s Next?

Here are some more interesting and in-depth resources for you to explore the libraries used for the healthcare solution proposed in the blog:

Relevant oneAPI Toolkits

• Intel® oneAPI Base Toolkit
  
  
• Intel® AI Analytics Toolkit (AI Kit)

Documentations & GitHub repositories

• oneDNN Documentation (GitHub)
  
  
• oneDAL Documentation (GitHub)
  
  
• oneMKL Documentation (GitHub)
  
  
• oneTBB Documentation (GitHub)
  
  
• oneDPL Documentation (GitHub)

We also encourage you to delve into other AI, HPC, and Rendering tools in Intel’s oneAPI-powered software portfolio.

About The Author

Deepthi A J is an author, educator, and researcher with a Doctoral degree from the IICSE University, Delaware. She is committed to the pursuit of knowledge and its applications in our lives.